Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys

The Arctic is facing drastic climate changes that are not correctly represented by state-of-the-art models because of complex feedbacks between radiation, clouds and sea-ice surfaces. A better understanding of the surface energy budget requires radiative measurements that are limited in time and space in the High Arctic (> 80° N) and mostly obtained through specific expeditions. Six years of lidar observations onboard buoys drifting in the Arctic Ocean above 83° N have been carried out as part of the IAOOS (Ice Atmosphere arctic Ocean Operating System) project. The objective of this study is to investigate the possibility to extent the IAOOS dataset to provide estimates of the shortwave (SW) and longwave (LW) surface irradiances from lidar measurements on drifting buoys. Our approach relies on the use of the STREAMER radiative transfer model to estimate the downwelling SW scattered radiances from the background noise measured by lidar. Those radiances are then used to derive estimates of the cloud optical depths. In turn, the knowledge of the cloud optical depth enables to estimate the SW and LW (using additional IAOOS measured information) downwelling irradiances at the surface. The method was applied to the IAOOS buoy measurements in spring 2015, and retrieved cloud optical depths were compared to those derived from radiative irradiances measured during the N-ICE (Norwegian Young Sea Ice Experiment) campaign at the meteorological station, in the vicinity of the drifting buoys. Retrieved and measured SW and LW irradiances were then compared. Results showed overall good agreement. Cloud optical depths were estimated with a rather large dispersion of about 47 %. LW irradiances showed a fairly small dispersion (within 5 W m−2), with a corrigible residual bias (3 W m−2). The estimated uncertainty of the SW irradiances was 4 %. But, as for the cloud optical depth, the SW irradiances showed the occurrence of a few outliers, that may be due to a short lidar sequence acquisition time (no more than four times 10 mn per day), possibly not long enough to smooth out cloud heterogeneity. The net SW and LW irradiances are retrieved within 13 W m−2.

A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

Validation of Drifting Buoy Data for Ocean Wave Observation

Drifting buoys collect wave data in the open ocean far from land and in areas with strong currents. However, the validation of the drifting buoy wave data is limited. Here, we compared the drifting buoy wave data, ERA5 wave data, and moored GPS buoy wave data. Data from 2009 to 2018 near the coast of Japan were used. The agreement of the drifting buoy-observed wave parameters with the moored GPS buoy-observed wave parameters is better than that of ERA5 wave parameters, which is statistically significant. In particular, the accuracy of the ERA5 wave heights tends to be lower where the ocean currents are fast. On the other hand, the agreement between the drifting buoy-observed wave heights and the moored GPS buoy-observed wave heights was good even in the areas with strong currents. It is confirmed that the drifting buoy wave data can be used as reference data for wave modeling study.

Measuring Global-Mean Sea-Level Rise With Surface Drifting Buoys

We propose to establish a new ocean observing system for monitoring global and regional mean sea-level changes. This system will consist of a global array of thousands of water-following drifting buoys tracked by a global navigation satellite system—such as the Global Positioning System (GPS)—which will continuously provide the geographical positions and the height of the sea surface along the buoys' trajectories. The sea-level height data collected in this way, averaged over regional basins and the global ocean, will provide daily measures of regional and global mean sea levels. An essential climate variable, mean sea level is an intrinsic measure of climate change, integrating the thermal expansion of the ocean's waters and additions to the ocean's mass from melting terrestrial ice. The realization of this new system requires that standardized vertical position measurements with controlled accuracy be acquired and regularly transmitted from relatively small and expendable drifting buoys, which constitutes a technological challenge, yet one with a clear path for being met. The development and implementation of this ocean shot concept will ultimately provide an independent, resilient, sustainable, and economical observational system to quantify natural and anthropogenic sea-level changes, augmenting the existing satellites and tide gauge observing systems.

Validation of atmospheric reanalyses over the Weddell Sea, Antarctica, using observations from drifting buoys

<p> </p><p>Global atmospheric reanalyses are frequently used to drive ocean-ice models but few data are available to assess the quality of these products in the Antarctic sea ice zone. We utilise measurements from three drifting buoys that were deployed on sea ice in the southern Weddell Sea in the austral summer of 2016 to validate the representation of near-surface atmospheric conditions in the ERA-Interim and ERA5 reanalyses produced by the European Centre for Medium Range Weather Forecasts (ECMWF). The buoys carried sensors to measure atmospheric pressure, air temperature and humidity, wind speed and direction, and downwelling shortwave and longwave radiation. One buoy remained in coastal fast ice for most of 2016 while the other two drifted northward through the austral winter and exited the pack ice during the following austral summer. Comparison of buoy measurements with reanalysis data indicates that both reanalyses represent the surface pressure field in this region accurately. Reanalysis temperatures are, however, biased warm by around 2 °C in both products, with the largest biases seen at the lowest temperatures. We suggest that this bias is a result of the simplified representation of sea ice in the reanalyses, in particular the lack of an insulating snow layer on top of the ice. We use a simple surface energy balance model to investigate the impact of the reanalysis biases on sea ice thermodynamics.</p>

Historical Estimates of Surface Marine Temperatures

Surface temperature documents our changing climate, and the marine record represents one of the longest widely distributed, observation-based estimates. Measurements of near-surface marine air temperature and sea-surface temperature have been recorded on platforms ranging from sailing ships to autonomous drifting buoys. The raw observations show an imprint of differing measurement methods and are sparse in certain periods and regions. This review describes how the real signal of global climate change can be determined from these sparse and noisy observations, including the quantification of measurement method–dependent biases and the reduction of spurious signals. Recent progress has come from analysis of the observations at increasing levels of granularity and from accounting for artifacts in the data that depend on platform types, measurement methods, and environmental conditions. Cutting across these effects are others caused by how the data were recorded, transcribed, and archived. These insights will be integrated into the next generation of global products quantified with validated estimates of uncertainty and the dependencies of its correlation structure. Further analysis of these records using improved data, metadata, and methods will certainly uncover more idiosyncrasies and new ways to improve the record.